{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RGUAFTUWHD353ZZLVVPCXKDIFZ","short_pith_number":"pith:RGUAFTUW","schema_version":"1.0","canonical_sha256":"89a802ce9638f7dde72bad5e2ba8682e4eef61b2d01e093aabc4c16b84bdf070","source":{"kind":"arxiv","id":"2401.08079","version":1},"attestation_state":"computed","paper":{"title":"Adversarial Masking Contrastive Learning for vein recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangxiang Yang, Huafeng Qin, Jun Wang, Mounim A. El-Yacoubi, Yiquan Wu","submitted_at":"2024-01-16T03:09:45Z","abstract_excerpt":"Vein recognition has received increasing attention due to its high security and privacy. Recently, deep neural networks such as Convolutional neural networks (CNN) and Transformers have been introduced for vein recognition and achieved state-of-the-art performance. Despite the recent advances, however, existing solutions for finger-vein feature extraction are still not optimal due to scarce training image samples. To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2401.08079","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-16T03:09:45Z","cross_cats_sorted":[],"title_canon_sha256":"9e2715c5aa71e3a196dd90d4d773124edc5ab09171c05b4f62e9f1e260d5bdc6","abstract_canon_sha256":"64bb1b5aca8dfe315cd46133b4f40dc4cf21284b49c7b081f2df4af358277051"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:34:06.944093Z","signature_b64":"HeTOxh5S0wZZ1Wt4oolmZrRNJ07mk6L3h/7j0fssqvA3s8xk5RUPSMVJW+OSQaNaLamoAbkGdUNli5MnBZQbCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89a802ce9638f7dde72bad5e2ba8682e4eef61b2d01e093aabc4c16b84bdf070","last_reissued_at":"2026-07-05T07:34:06.943612Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:34:06.943612Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Masking Contrastive Learning for vein recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangxiang Yang, Huafeng Qin, Jun Wang, Mounim A. El-Yacoubi, Yiquan Wu","submitted_at":"2024-01-16T03:09:45Z","abstract_excerpt":"Vein recognition has received increasing attention due to its high security and privacy. Recently, deep neural networks such as Convolutional neural networks (CNN) and Transformers have been introduced for vein recognition and achieved state-of-the-art performance. Despite the recent advances, however, existing solutions for finger-vein feature extraction are still not optimal due to scarce training image samples. To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08079","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.08079/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2401.08079","created_at":"2026-07-05T07:34:06.943676+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.08079v1","created_at":"2026-07-05T07:34:06.943676+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08079","created_at":"2026-07-05T07:34:06.943676+00:00"},{"alias_kind":"pith_short_12","alias_value":"RGUAFTUWHD35","created_at":"2026-07-05T07:34:06.943676+00:00"},{"alias_kind":"pith_short_16","alias_value":"RGUAFTUWHD353ZZL","created_at":"2026-07-05T07:34:06.943676+00:00"},{"alias_kind":"pith_short_8","alias_value":"RGUAFTUW","created_at":"2026-07-05T07:34:06.943676+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ","json":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ.json","graph_json":"https://pith.science/api/pith-number/RGUAFTUWHD353ZZLVVPCXKDIFZ/graph.json","events_json":"https://pith.science/api/pith-number/RGUAFTUWHD353ZZLVVPCXKDIFZ/events.json","paper":"https://pith.science/paper/RGUAFTUW"},"agent_actions":{"view_html":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ","download_json":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ.json","view_paper":"https://pith.science/paper/RGUAFTUW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.08079&json=true","fetch_graph":"https://pith.science/api/pith-number/RGUAFTUWHD353ZZLVVPCXKDIFZ/graph.json","fetch_events":"https://pith.science/api/pith-number/RGUAFTUWHD353ZZLVVPCXKDIFZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ/action/storage_attestation","attest_author":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ/action/author_attestation","sign_citation":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ/action/citation_signature","submit_replication":"https://pith.science/pith/RGUAFTUWHD353ZZLVVPCXKDIFZ/action/replication_record"}},"created_at":"2026-07-05T07:34:06.943676+00:00","updated_at":"2026-07-05T07:34:06.943676+00:00"}